def data():
preprocess = preProcessing()
max_claims_length = 65
max_sents_length = 138
dataset_name = 'fever_sup'
nb_classes = 1
train_data = pickle.load(
open("./bert/datasets/train_" + str(dataset_name) + ".pkl", "rb"))
x_claim = pickle.load(
open(
"./bert/new_embeddings/train_claims_" + str(dataset_name) +
"_embed" + ".pkl", "rb"))
x_sents = pickle.load(
open(
"./bert/new_embeddings/train_sents_" + str(dataset_name) +
"_embed" + ".pkl", "rb"))
x_labels = train_data["lablel"]
if dataset_name == 'fever_3':
x_labels = np_utils.to_categorical(x_labels, nb_classes)
test_data = pickle.load(
open("./bert/datasets/test_" + str(dataset_name) + ".pkl", "rb"))
test_claims_data = pickle.load(
open(
"./bert/new_embeddings/test_claims_" + str(dataset_name) +
"_embed" + ".pkl", "rb"))
test_sents_data = pickle.load(
open(
"./bert/new_embeddings/test_sents_" + str(dataset_name) +
"_embed" + ".pkl", "rb"))
test_labels = test_data["lablel"]
if dataset_name == 'fever_3':
test_labels = np_utils.to_categorical(test_labels, nb_classes)
# x_claim, x_sents, x_labels = preprocess.to_padding(claim_embeddings, sents_embeddings, labels, max_claims_length, max_sents_length)
# test_claims_data, test_sents_data, test_labels = preprocess.to_padding(test_claim_embeddings, test_sents_embeddings, test_labels, max_claims_length, max_sents_length)
return (x_claim, x_sents, x_labels, test_claims_data, test_sents_data,
test_labels)
def lstm_model(x_claim, x_sents, x_labels, test_claims_data, test_sents_data,
test_labels):
claim_length = 65
sents_length = 138
embedding_dim = 768
nb_classes = 1
preprocess = preProcessing()
claims_input = Input(shape=(claim_length, embedding_dim),
dtype='float32',
name='claims')
encoded_claims = LSTM({{choice([8, 16, 64, 128, 256, 512, 1024])}},
return_sequences=True,
recurrent_dropout={{uniform(0, 1)}},
dropout={{uniform(0, 1)}})(claims_input)
encoded_claims = LSTM({{choice([8, 16, 64, 128, 256, 512, 1024])}},
recurrent_dropout={{uniform(0, 1)}},
dropout={{uniform(0, 1)}})(encoded_claims)
sentences_input = Input(shape=(sents_length, embedding_dim),
dtype='float32',
name='sentences')
encoded_sentences = LSTM({{choice([8, 16, 64, 128, 256, 512, 1024])}},
return_sequences=True,
recurrent_dropout={{uniform(0, 1)}},
dropout={{uniform(0, 1)}})(sentences_input)
encoded_sentences = LSTM({{choice([16, 64, 256, 128, 512, 1024])}},
recurrent_dropout={{uniform(0, 1)}},
dropout={{uniform(0, 1)}})(encoded_sentences)
concatenate_layers = concatenate([encoded_claims, encoded_sentences],
axis=-1)
concatenate_layers = Dropout({{uniform(0, 1)}})(concatenate_layers)
concatenate_layers = Dense({{choice([8, 16, 32, 64, 256, 512, 1024])}},
kernel_regularizer=regularizers.l2(0.001),
activation='relu')(concatenate_layers)
concatenate_layers = Dense({{choice([8, 16, 32, 64, 256, 512, 1024])}},
kernel_regularizer=regularizers.l2(0.001),
activation='relu')(concatenate_layers)
# concatenate_layers = Dense(32, kernel_regularizer=regularizers.l2(0.001), activation='relu')(concatenate_layers)
concatenate_layers = Dropout({{uniform(0, 1)}})(concatenate_layers)
if nb_classes == 3:
pred_label = Dense(3, activation='softmax')(concatenate_layers)
else:
pred_label = Dense(1, activation='sigmoid')(concatenate_layers)
model = Model([claims_input, sentences_input], pred_label)
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
checkpointer = ModelCheckpoint(filepath='bert_keras_weights_feverbin.h5',
verbose=1,
save_best_only=True)
model.compile(loss='binary_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
model.fit({
'claims': x_claim,
'sentences': x_sents
},
x_labels,
batch_size={{choice([64, 128])}},
epochs=20,
verbose=2,
validation_split=0.1,
callbacks=[early_stopping, checkpointer])
score, acc = model.evaluate(
{
'claims': test_claims_data,
'sentences': test_sents_data
},
test_labels,
verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
train_data = pd.DataFrame(data=tmp_dict)
return train_data
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='start training')
parser.add_argument('task',
choices=['sent_retrieval', 'claim_classification'],
help="what task should be performed?")
task = parser.parse_args().task
preprocess = preProcessing()
training = TrainModel()
metrics = Metrics()
max_claims_length = 40
max_sents_length = 250
if task == 'sent_retrieval':
nb_classes = 2
train_dataset_name = "fever_full_binary_train"
test_dataset_name = "fever_full_binary_dev"
train_data = "/scratch/kkuma12s/github/fact-validation/thesis-code/Proof_Extraction/data/fever-full/" + train_dataset_name + ".jsonl"
elif task == 'claim_classification':
print("claim classification selected ")